Everyone with a bank account has a bank statement; it is a universal document that has all your transaction history and is unequivocally the most valid record of one’s financial health. Thus, bank statement analysis is an important tool for underwriting borrowers effectively - the key challenge is the lack of automation and effective data categorisation and classification.
Suffice it to say that the world has changed - the pandemic has shifted our transaction behaviours. On the income side, ‘three recent payslips’ won’t necessarily identify where income has changed substantially over the last month or fortnight or how it plays out in the broader context of a customer’s peer group. On the expense side, defining a ‘new normal' becomes even more complex. Consumer behaviour in the wake of the pandemic is likely to be altered completely by the experience.
Keeping up with these changes in behaviour and making sense of what it means for their creditworthiness is crucial. The only way to keep up is to leverage high-velocity transaction data to give an accurate snapshot of each customer/business’ unique situation - a real-time ‘profit and loss statement’ so to speak.
For example, using FinBox’s BankConnect to curate and categorise expenditure data, we’re able to capture minute details and identify patterns about their transactions, which traditionally might be missed.
Let’s break down what factors are indicative of creditworthiness and fraudulent behaviour. This will also serve as a guide on what to look for in a bank statement analyser.
Withdrawals and liabilities score the highest in transaction analysis. Sudden withdrawals of large amounts or several withdrawals of similar amounts at regular intervals can be a potential red flag - a recurring expense, an undisclosed loan or debt that needs to be paid off. Liabilities combined with the credit score (CIBIL score + alternate data scorecards) can show a clear picture of the customer’s repayment behaviour. This seems like the most obvious factor to analyse, but the catch is classifying new-age lenders.
Lender classification is a crucial factor - the lending ecosystem has evolved and digital lenders have facilitated $2.2 billion in digital loans in 2021-22 alone. Factoring in consumers’ obligations towards these digital lenders then is crucial but is usually left out in traditional transaction analysis. So far, FinBox’s BankConnect has been able to identify 30% more obligations than our competitors. Customers diligently paying their BNPL loans is pivotal in deciding their creditworthiness.
A basic analysis of the bank statement would show salary classifications under tags like ‘payroll’ or ‘salary’ or ‘wage’. The time of the month of credit is a crucial factor. But what happens if these tags aren’t present? Or what happens when there is recurring credit from the same source every month but the amount differs? Or what happens when the credit happens at different times every month? Not determining the salary then, will mean fewer customers get approved for loans. Think about it, can the Average Bank Balance (ABB) be enough to determine if a customer is creditworthy or not? One could be earning well but have a flair for spending. This is where a product like FinBox BankConnect can help - we’ve developed a formula to account for standard deviations in recurring credits to ensure that lenders get an accurate Fixed Obligation to Income Ratio (FOIR) and the full picture of its potential customer.
We’ve been able to identify 25% more salary classifications than our competitors. Not only does it help determine salary/income, but it acts as an essential factor in determining to underwrite. More carefully culled data means better underwriting, which then means more customers and improved revenues.
The pandemic moved everything online, everything from hailing an autorickshaw to ordering food and groceries. For lenders that want to streamline their process, it’s important to create user personas/behavioural profiles. One pivotal factor then is to consider where your customers are spending their money and how much they’ve spent. This is critical to underwriting to determine whether your potential customer is spending money on gambling or on essentials like groceries. This also helps identify potential fraud as lenders gain insight into where their potential customers’ money is going.
Quality and adaptability
Digital lenders are on the rise and with that, banks’ adaptability to the changing world of money has also increased. New bank statement formats and new income/expense classifications and categories are constant. Adaptability transaction analysis models are critical, at FinBox we add nearly 50 new templates every day to quickly adapt to the fast-changing bank statement landscape. What’s more? BankConnect is also able to identify any lapses that may occur while uploading a bank statement - whether it be recognising if statements for the requested period are uploaded or not; if the right lender’s statement is uploaded; or if the uploaded document is a bank statement at all. All this happens quietly in the background, within seconds.
One of the most common types of fraud we find in bank statements is author fraud. Every PDF document has author details metadata. Based on the data we’ve collected, we know when a PDF is edited based on the metadata of the statement. FinBox’s BankConnect checks for document tampering, whether or not there’s a mismatch in the user’s details and the account details provided, whether the transactions in the bank statements match another bank statement, etc.
What is the competitive advantage of superior transactional analysis for lenders?
Most lenders have this data, but they’re not mined to extract maximum predictive value
Better classification and more precise scorecards can help underwrite borrowers better - it can even help lenders sharpen their offers and decide things like borrowers’ maximum liability potential every month etc.
Speed is crucial in instant loan generation. But getting underwriting right is just as crucial and doing it in a way without any lags in the user journey is even more important.
FinBox has been able to reduce drop-offs by 30% with our real-time bank statement processing product - BankConnect. A 1000-page document that can be processed, categorised and analysed within seconds is an edge that could separate you from the herd.
What’s the appeal of transaction analysis for lenders and why now?
Highly accessible and clean data
Big data analytics capabilities have ensured easier management of insights
Bank statement data is not controversial -it meets regulatory standards
It can be used to make multiple decisions - Onboarding, determining credit limit, recovery treatment, fraud detection ‘
Digital bank statement analysis also ensures audit trails and easy compliance with the relevant regulatory laws and guidelines
How FinBox can help
Our subject matter specialists have created intuitive, AI-driven, automated tools like BankConnect AA. We examine bank-specific narratives, layouts and logic and it's designed to support bank systems across the spectrum. We’re already working with 250+ lenders who’ve helped us transform transaction data to an enterprise-grade structured data asset that has in turn helped us access powerful insights based on up-to-the-minute transaction data. We’re uniquely positioned to deploy BankConnect within your organisation to protect your customers and your bottom line.